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C8200 | Use this type of sampling to indicate if a particular trait or characteristic exists in a population. Researchers widely use the non-probability sampling method when they aim at conducting qualitative research, pilot studies, or exploratory research. | |
C8201 | This approach works when the sample size is relatively large (greater than or equal to 30). Use the first or third formulas when the population size is known.How to Choose Sample Size for a Simple Random Sample.Sample statisticPopulation sizeSample sizeProportionUnknownn = [ ( z2 * p * q ) + ME2 ] / ( ME2 )3 more rows | |
C8202 | In order to be considered a normal distribution, a data set (when graphed) must follow a bell-shaped symmetrical curve centered around the mean. It must also adhere to the empirical rule that indicates the percentage of the data set that falls within (plus or minus) 1, 2 and 3 standard deviations of the mean. | |
C8203 | The term “sigmoid” means S-shaped, and it is also known as a squashing function, as it maps the whole real range of z into [0,1] in the g(z). This simple function has two useful properties that: (1) it can be used to model a conditional probability distribution and (2) its derivative has a simple form. | |
C8204 | In stratified sampling, a sample is drawn from each strata (using a random sampling method like simple random sampling or systematic sampling). In cluster sampling, the sampling unit is the whole cluster; Instead of sampling individuals from within each group, a researcher will study whole clusters. | |
C8205 | How It Works. Connected component labeling works by scanning an image, pixel-by-pixel (from top to bottom and left to right) in order to identify connected pixel regions, i.e. regions of adjacent pixels which share the same set of intensity values V. | |
C8206 | K-means is an unsupervised learning algorithm as it infers a clustering (or labels) for a set of provided samples that do not initially have labels. The goal of k-means is to partition the n samples from your dataset in to k clusters where each datapoint belongs to the single cluster for which it is nearest to. | |
C8207 | Each feature, or column, represents a measurable piece of data that can be used for analysis: Name, Age, Sex, Fare, and so on. Features are also sometimes referred to as “variables” or “attributes.” Depending on what you're trying to analyze, the features you include in your dataset can vary widely. | |
C8208 | Backtracking is a general algorithm for finding all (or some) solutions to some computational problems, notably constraint satisfaction problems, that incrementally builds candidates to the solutions, and abandons a candidate ("backtracks") as soon as it determines that the candidate cannot possibly be completed to a | |
C8209 | Batch means a group of training samples. In gradient descent algorithms, you can calculate the sum of gradients with respect to several examples and then update the parameters using this cumulative gradient. If you 'see' all training examples before one 'update', then it's called full batch learning. | |
C8210 | A pooling layer is another building block of a CNN. Its function is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network. Pooling layer operates on each feature map independently. The most common approach used in pooling is max pooling. | |
C8211 | Getting and preparing the data Each line of the text file contains a list of labels, followed by the corresponding document. All the labels start by the __label__ prefix, which is how fastText recognize what is a label or what is a word. The model is then trained to predict the labels given the word in the document. | |
C8212 | Constrained optimization problems are problems for which a function is to be minimized or maximized subject to constraints . Here is called the objective function and is a Boolean-valued formula. stands for "maximize subject to constraints ". You say a point satisfies the constraints if is true. | |
C8213 | For a sequence of random variables {Xn}, if there exists a real number c such that for every small positive number σ the probability that the absolute difference between Xn and c is less than σ has the limit of 1 when n → ∞, namely, then we say that {xn } converges in probability to constant c, and c is called the | |
C8214 | A 'weak' learner (classifer, predictor, etc) is just one which performs relatively poorly--its accuracy is above chance, but just barely. Weak learner also suggests that many instances of the algorithm are being pooled (via boosting, bagging, etc) together into to create a "strong" ensemble classifier. | |
C8215 | Batch normalization (also known as batch norm) is a method used to make artificial neural networks faster and more stable through normalization of the input layer by re-centering and re-scaling. It was proposed by Sergey Ioffe and Christian Szegedy in 2015. | |
C8216 | The estimated regression equations show the equation for y hat i.e. predicted y. The regression model on the other hand shows equation for the actual y. This is an abstract model and uses population terms (which are specified in Greek symbols). | |
C8217 | The false alarm probability is the probability that exceeds a certain threshold when there is no signal. | |
C8218 | Connectionism theory is based on the principle of active learning and is the result of the work of the American psychologist Edward Thorndike. This work led to Thorndike's Laws. According to these Laws, learning is achieved when an individual is able to form associations between a particular stimulus and a response. | |
C8219 | A function that represents a discrete probability distribution is called a probability mass function. A function that represents a continuous probability distribution is called a probability density function. Functions that represent probability distributions still have to obey the rules of probability. | |
C8220 | Euclidean distance | |
C8221 | Bellman equation is the basic block of solving reinforcement learning and is omnipresent in RL. It helps us to solve MDP. To solve means finding the optimal policy and value functions. The optimal value function V*(S) is one that yields maximum value. | |
C8222 | A) (ii) Disadvantages of Mohr Method Mohr's method is suitable only for titration of chloride, bromide and cyanide alone. Errors can be introduced due to the need of excess titrant before the endpoint colour is visible. | |
C8223 | Gradient Boosting Machines vs. XGBoost. While regular gradient boosting uses the loss function of our base model (e.g. decision tree) as a proxy for minimizing the error of the overall model, XGBoost uses the 2nd order derivative as an approximation. | |
C8224 | A Bloom filter is a data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set. The price paid for this efficiency is that a Bloom filter is a probabilistic data structure: it tells us that the element either definitely is not in the set or may be in the set. | |
C8225 | The weaknesses of decision tree methods : Decision trees are less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute. Decision trees are prone to errors in classification problems with many class and relatively small number of training examples. | |
C8226 | They are data records that differ dramatically from all others, they distinguish themselves in one or more characteristics. In other words, an outlier is a value that escapes normality and can (and probably will) cause anomalies in the results obtained through algorithms and analytical systems. | |
C8227 | Gradient is a vector that is tangent of a function and points in the direction of greatest increase of this function. Gradient is zero at a local maximum or minimum because there is no single direction of increase. In mathematics, gradient is defined as partial derivative for every input variable of function. | |
C8228 | In a dataset a training set is implemented to build up a model, while a test (or validation) set is to validate the model built. Data points in the training set are excluded from the test (validation) set. | |
C8229 | robust is a programmer's command that computes a robust variance estimator based on a varlist of equation-level scores and a covariance matrix. The robust variance estimator goes by many names: Huber/White/sandwich are typically used in the context of robustness against heteroskedasticity. | |
C8230 | Random assignment helps reduce the chances of systematic differences between the groups at the start of an experiment and, thereby, mitigates the threats of confounding variables and alternative explanations. However, the process does not always equalize all of the confounding variables. | |
C8231 | The equation used to calculate kappa is: Κ = PR(e), where Pr(a) is the observed agreement among the raters and Pr(e) is the hypothetical probability of the raters indicating a chance agreement. The formula was entered into Microsoft Excel and it was used to calculate the Kappa coefficient. | |
C8232 | Factor analysis is a way to condense the data in many variables into a just a few variables. For this reason, it is also sometimes called “dimension reduction.” You can reduce the “dimensions” of your data into one or more “super-variables.” The most common technique is known as Principal Component Analysis (PCA). | |
C8233 | Hyperplanes are decision boundaries that help classify the data points. Data points falling on either side of the hyperplane can be attributed to different classes. Also, the dimension of the hyperplane depends upon the number of features. | |
C8234 | “A method of estimating the parameters of a distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable.” | |
C8235 | Medical Definition of alpha state : a state of wakeful relaxation that is associated with increased alpha wave activity When electroencephalograms show a brain wave pattern of 9 to 12 cycles per second, the subject is said to be in alpha state, usually described as relaxed, peaceful, or floating.— | |
C8236 | Markov models are useful to model environments and problems involving sequential, stochastic decisions over time. Representing such environments with decision trees would be confusing or intractable, if at all possible, and would require major simplifying assumptions [2]. | |
C8237 | If the mean more accurately represents the center of the distribution of your data, and your sample size is large enough, use a parametric test. If the median more accurately represents the center of the distribution of your data, use a nonparametric test even if you have a large sample size. | |
C8238 | There are essentially three stopping criteria that can be adopted to stop the K-means algorithm: Centroids of newly formed clusters do not change. Points remain in the same cluster. Maximum number of iterations are reached. | |
C8239 | Scikit-learn is a free machine learning library for Python. It features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports Python numerical and scientific libraries like NumPy and SciPy . Then we'll dive into scikit-learn and use preprocessing. | |
C8240 | Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). | |
C8241 | A generalized linear model is a flexible generalization of ordinary linear regression models which allows for the response variables (dependent) to have error distribution other than normal distribution. GLM was developed to unify other statistical methods (linear, logistic, Poisson regression). | |
C8242 | The exponential distribution is one of the widely used continuous distributions. It is often used to model the time elapsed between events. We will now mathematically define the exponential distribution, and derive its mean and expected value. | |
C8243 | This term is used in statistics in its ordinary sense, but most frequently occurs in connection with samples from different populations which may or may not be identical. If the populations are identical they are said to be homogeneous, and by extension, the sample data are also said to be homogeneous. | |
C8244 | When your child sits the eleven plus exam, the number of questions answered correctly decides the "Raw Score". If there are more than one tests, the score may be the sum of the raw scores. A standardized test score is calculated by translating the raw score into a completely different scale. | |
C8245 | The Cox proportional hazards model92 is the most popular model for the analysis of survival data. It is a semiparametric model; it makes a parametric assumption concerning the effect of the predictors on the hazard function, but makes no assumption regarding the nature of the hazard function λ(t) itself. | |
C8246 | Precision refers to how close estimates from different samples are to each other. For example, the standard error is a measure of precision. When the standard error is small, estimates from different samples will be close in value; and vice versa. | |
C8247 | The Bernoulli distribution represents the success or failure of a single Bernoulli trial. The Binomial Distribution represents the number of successes and failures in n independent Bernoulli trials for some given value of n. Another example is the number of heads obtained in tossing a coin n times. | |
C8248 | Can you list out the critical assumptions of linear regression? What is Heteroscedasticity? What is the primary difference between R square and adjusted R square? Can you list out the formulas to find RMSE and MSE? | |
C8249 | Each node in the decision tree works on a random subset of features to calculate the output. The random forest then combines the output of individual decision trees to generate the final output. The Random Forest Algorithm combines the output of multiple (randomly created) Decision Trees to generate the final output. | |
C8250 | Classification is one of the most fundamental concepts in data science. Classification algorithms are predictive calculations used to assign data to preset categories by analyzing sets of training data.၂၀၂၀၊ ဩ ၂၆ | |
C8251 | Retail. Supermarkets, for example, use joint purchasing patterns to identify product associations and decide how to place them in the aisles and on the shelves. Data mining also detects which offers are most valued by customers or increase sales at the checkout queue. | |
C8252 | The number of true positives is placed in the top left cell of the confusion matrix. The data rows (emails) belonging to the positive class (spam) and incorrectly classified as negative (normal emails). These are called False Negatives (FN). | |
C8253 | Frequency tables, pie charts, and bar charts are the most appropriate graphical displays for categorical variables. Below are a frequency table, a pie chart, and a bar graph for data concerning Penn State's undergraduate enrollments by campus in Fall 2017. Note that in the bar chart, the bars are separated by a space. | |
C8254 | Lab Color is a more accurate color space. It specifies a color using a 3-axis system. The a-axis (green to red), b-axis (blue to yellow) and Lightness axis. The best thing about Lab Color is that it's device-independent. That means that it's easier to achieve exactly the same color across different media. | |
C8255 | Markov analysis is a method used to forecast the value of a variable whose predicted value is influenced only by its current state, and not by any prior activity. In essence, it predicts a random variable based solely upon the current circumstances surrounding the variable. | |
C8256 | Population variance (σ2) tells us how data points in a specific population are spread out. Here N is the population size and the xi are data points. μ is the population mean. | |
C8257 | Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. | |
C8258 | Classification Accuracy It is the ratio of number of correct predictions to the total number of input samples. It works well only if there are equal number of samples belonging to each class. For example, consider that there are 98% samples of class A and 2% samples of class B in our training set. | |
C8259 | The best way to fix it is to perform a log transform of the same data, with the intent to reduce the skewness. After taking logarithm of the same data the curve seems to be normally distributed, although not perfectly normal, this is sufficient to fix the issues from a skewed dataset as we saw before. | |
C8260 | Key TakeawaysA Bernoulli (success-failure) experiment is performed n times, and the trials are independent.The probability of success on each trial is a constant p ; the probability of failure is q=1−p q = 1 − p .The random variable X counts the number of successes in the n trials. | |
C8261 | Particle filtering uses a set of particles (also called samples) to represent the posterior distribution of some stochastic process given noisy and/or partial observations. The state-space model can be nonlinear and the initial state and noise distributions can take any form required. | |
C8262 | Firstly generate observations from a standard normal distribution then multiply by the standard deviation and add the mean. | |
C8263 | No, the normal distribution cannot be skewed. It is a symmetric distribution with mean, median and mode being equal. However, a small sample from a normally distributed variable may be skewed. | |
C8264 | The purpose of the activation function is to introduce non-linearity into the output of a neuron. We know, neural network has neurons that work in correspondence of weight, bias and their respective activation function. | |
C8265 | Confirmation bias can lead even the most experienced experts astray. Doctors, for example, will sometimes get attached to a diagnosis and then look for evidence of the symptoms they suspect already exist in a patient while ignoring markers of another disease or injury. | |
C8266 | Inferential statistics takes data from a sample and makes inferences about the larger population from which the sample was drawn. | |
C8267 | AI has the potential to accelerate the process of achieving the global education goals through reducing barriers to access learning, automating management processes, and optimizing methods in order to improve learning outcomes. | |
C8268 | This type of distribution is useful when you need to know which outcomes are most likely, the spread of potential values, and the likelihood of different results. In this blog post, you'll learn about probability distributions for both discrete and continuous variables. | |
C8269 | Six quick tips to improve your regression modelingA.1. Fit many models. A.2. Do a little work to make your computations faster and more reliable. A.3. Graphing the relevant and not the irrelevant. A.4. Transformations. A.5. Consider all coefficients as potentially varying. A.6. Estimate causal inferences in a targeted way, not as a byproduct of a large regression. | |
C8270 | SVMs (linear or otherwise) inherently do binary classification. However, there are various procedures for extending them to multiclass problems. A binary classifier is trained for each pair of classes. A voting procedure is used to combine the outputs. | |
C8271 | the state of being likely or probable; probability. a probability or chance of something: There is a strong likelihood of his being elected. | |
C8272 | Difference between deep learning and reinforcement learning The difference between them is that deep learning is learning from a training set and then applying that learning to a new data set, while reinforcement learning is dynamically learning by adjusting actions based in continuous feedback to maximize a reward. | |
C8273 | Logarithmic Loss, or simply Log Loss, is a classification loss function often used as an evaluation metric in Kaggle competitions. Log Loss quantifies the accuracy of a classifier by penalising false classifications. | |
C8274 | Decision tree is unstable because training a tree with a slightly different sub-sample causes the structure of the tree to change drastically. It overfits by learning from noise data as well and optimises for that particular sample, which causes its variable importance order to change significantly. | |
C8275 | Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor. | |
C8276 | A spectrum is simply a chart or a graph that shows the intensity of light being emitted over a range of energies. Spectra can be produced for any energy of light, from low-energy radio waves to very high-energy gamma rays. Each spectrum holds a wide variety of information. | |
C8277 | So the standard error of a mean provides a statement of probability about the difference between the mean of the population and the mean of the sample. This is called the 95% confidence interval , and we can say that there is only a 5% chance that the range 86.96 to 89.04 mmHg excludes the mean of the population. | |
C8278 | For independent random variables X and Y, the variance of their sum or difference is the sum of their variances: Variances are added for both the sum and difference of two independent random variables because the variation in each variable contributes to the variation in each case. | |
C8279 | Exponential beta value is interpreted with the reference category, where the probability of the dependent variable will increase or decrease. In continuous variables, it is interpreted with one unit increase in the independent variable, corresponding to the increase or decrease of the units of the dependent variable. | |
C8280 | Among all continuous probability distributions with support [0, ∞) and mean μ, the exponential distribution with λ = 1/μ has the largest differential entropy. In other words, it is the maximum entropy probability distribution for a random variate X which is greater than or equal to zero and for which E[X] is fixed. | |
C8281 | Batch normalization is a technique that can improve the learning rate of a neural network. It does so by minimizing internal covariate shift which is essentially the phenomenon of each layer's input distribution changing as the parameters of the layer above it change during training. | |
C8282 | Knowledge-representation is a field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used to solve complex problems. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system. | |
C8283 | In general, K-means is a heuristic algorithm that partitions a data set into K clusters by minimizing the sum of squared distance in each cluster. In this paper, the simulation of basic k-means algorithm is done, which is implemented using Euclidian distance metric. | |
C8284 | Like machine learning or deep learning, NLP is a subset of AI. SAS offers a clear and basic explanation of the term: “Natural language processing makes it possible for humans to talk to machines.” It's the branch of AI that enables computers to understand, interpret, and manipulate human language. | |
C8285 | Regularized regression is a type of regression where the coefficient estimates are constrained to zero. The magnitude (size) of coefficients, as well as the magnitude of the error term, are penalized. “Regularization” is a way to give a penalty to certain models (usually overly complex ones). | |
C8286 | Non-linearity in neural networks simply mean that the output at any unit cannot be reproduced from a linear function of the input. | |
C8287 | A low pass filter is a fixed filter just filters out frequencies above a passband. A Kalman filter can be used for state estimation, prediction of values in time and smoothing. A Kalman filter is a consequence of state variable models and LQG system theory. It has a gain which changes at each time step. | |
C8288 | Ridge regression has two main benefits. First, adding a penalty term reduces overfitting. Second, the penalty term guarantees that we can find a solution. I think the second part is easier to explain. | |
C8289 | While the previous study (Wu et al., 2015) suggests that ingroup derogation is a specialized mechanism which disregards explicit disease-relevant information mediated by outgroup members, a different pattern was observed in Experiment 2. | |
C8290 | This is because of the logistic distribution having heavier tails (than the normal distribution): Any outliers would not carry as much weight under the assumptions of the logistic (blue) distribution. In a logistic regression does a very small P value for a predictor mean a good predictor or a bad predictor? | |
C8291 | A linear regression model extended to include more than one independent variable is called a multiple regression model. It is more accurate than to the simple regression. The principal adventage of multiple regression model is that it gives us more of the information available to us who estimate the dependent variable. | |
C8292 | Econometrics is often “theory driven” while statistics tends to be “data driven”. Typically, econometricians test theory using data, but often do little if any exploratory data analysis. On the other hand, I tend to build models after looking at data sets. | |
C8293 | In probability theory and statistics, the marginal distribution of a subset of a collection of random variables is the probability distribution of the variables contained in the subset. It gives the probabilities of various values of the variables in the subset without reference to the values of the other variables. | |
C8294 | It might take about 2-4 hours of coding and 1-2 hours of training if done in Python and Numpy (assuming sensible parameter initialization and a good set of hyperparameters). No GPU required, your old but gold CPU on a laptop will do the job. Longer training time is expected if the net is deeper than 2 hidden layers. | |
C8295 | Collaborative filtering (CF) is a technique used by recommender systems. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). | |
C8296 | 0:365:49Suggested clip · 61 secondsTensorboard Explained in 5 Min - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C8297 | Leaving a long gap between testing periods would not really help. For a test-retest coefficient to be an accurate estimate of reliability, there should be no change to the underlying trait (i.e. memory ability). If a long delay is enforced, subjects' actual memory capacities will change. | |
C8298 | Many instances of binomial distributions can be found in real life. For example, if a new drug is introduced to cure a disease, it either cures the disease (it's successful) or it doesn't cure the disease (it's a failure). If you purchase a lottery ticket, you're either going to win money, or you aren't. | |
C8299 | 17:1525:32Suggested clip · 110 secondsStructural Equation Modeling: what is it and what can we use it for YouTubeStart of suggested clipEnd of suggested clip |
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